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Abstract

Background

Mental health has long been a neglected problem in global healthcare. The social and
economic impacts of conditions affecting the mind are still underestimated. However,
in recent years it is becoming more apparent that mental disorders are a growing global
concern and there is a necessity of developing novel services and researching effective
means of providing interventions to sufferers. Such novel services could include technology-based
solutions already used in other healthcare applications but are yet to make their
way into standard psychiatric practice.

Methods

This manuscript proposes a system where sensors are utilised to devise an “early warning”
system for patients with bipolar disorder. The system, containing wearable and environmental
sensors, would collect behavioural data independent from the patient’s self-report.
To test the feasibility of the concept, a prototype system was devised, which was
followed by trials including four healthy volunteers as well as a bipolar patient.

Results

The sensors utilised in the study yielded behavioural data which may be of significant
use in detecting early effects of a bipolar episode. Basic processing performed on
particular data inputs provided information about activity patterns in areas, which
are usually strongly influenced by the course of Bipolar Disorder.

Conclusions

The manuscript discusses the basic usage issues and other barriers which are to be
tackled before technology-based approaches to mental care can be successfully rolled
out and their true value appraised.

Keywords:

Introduction

Mental health has long been a neglected problem in global healthcare. The social and
economic impacts of conditions affecting the mind are still underestimated, much as
the problems of those who suffer from such illnesses were overlooked and their burden
trivialised. However, in recent years it is becoming more apparent that mental disorders
are a growing concern not only in the “developed world” but globally as well.

When considered in terms of mortality, mental disorders are not to be found among
the main priorities of public health [1,2]. This is largely due to the fact that the vast majority of neuropsychiatric conditions
(even if untreated) do not directly lead to death of the patient. Nevertheless in
extreme circumstances these illnesses can increase the risk of mortality (e.g. by
suicide) [2]. However, reports, which utilize more complex measures of quality of life to calculate
the burden of diseases, rank chronic mental conditions almost as high as respiratory
and cardiovascular conditions and higher than all types of cancer and HIV. Moreover,
it is projected that by 2030 mental disorders will constitute 15% of the global disease
burden with unipolar depression becoming the second highest occurring condition [2]. Considering the rising burden of such conditions, most health organisations and
other policy-makers, in their reports, point to the necessity of developing novel
services and researching non-costly and effective means of providing interventions
to sufferers [3].

Currently, clinical psychiatry (and psychology) relies greatly on the retrospective
self-reporting of patients’ symptoms. Such methods of data collection, whether it
is an end-of-day paper diary, simple interview or a structured questionnaire, have
one common feature in that the information must be recalled from the patient’s memory
[4]. This may lead to misjudgement of the patient’s state as studies show that gathering
information retrospectively is subject to multiple systematic distortions and biases
(e.g. towards socially acceptable answers) [5]. It is also known that “positive” events are more likely to be remembered than ones
with negative association for the respondent. The processing of memories is also connected
to the current mood, which introduces even more variability to the recollection process
[6]. To rectify the disadvantages of the retrospective method, in recent years, researchers
and clinicians have begun to adopt different approaches. The methodologies range from
the use of real-time electronic diaries, maintained by the user, to the use of sophisticated
technology to extract psycho-physiological information independent of the user’s input
[4]. However, the efficiency and effectiveness related to application of these ambulatory
assessment methods in general psychiatric practice is yet to be observed. This manuscript
presents a technology-based system aimed at facilitating self-care for people with
bipolar disorder (BD).

The nature of the BD condition is that patients can experience the extremes of low
mood and inactivity (depression phase), which then can swing to hyperactivity and
grandiosity in the manic phase. Although, BD is not the most widely spread psychiatric
condition, reports show that, among mental illnesses, BD alongside of schizophrenia
has the highest “disability rating”. This rating a result of apprising particular
conditions in terms of severity and length of disability caused regardless of whether
it is social, physical of other kind of disability [2]. Therefore, developing novel means of managing the condition is an important, interesting,
yet challenging goal for modern health services which potentially can be tackled using
means of personalized, pervasive computing.

Research on BD therapies as well as clinical evidence indicates that bipolar patients
are a group which is likely to benefit from a personal monitoring and treatment tool.
The main reasons are:

· In general, it was proven that even diary based “early warning system” against BD
can improve effectiveness of interventions [7]

· The occurrence of opposite extremes of behaviour, characterising the condition,
is likely to be apparent in physical and social activities possible to monitor via
pervasive technology [8].

· Unlike numerous psychiatric conditions patients with bipolar disorder possess in
general a high self-awareness and are willing to comply with treatment regime [8].

· There is a known link between the condition and increased creativity, which renders
the patient group as more likely to accept novel approaches to the management regime
[9,10].

Background

Modern developments in sensor technology enable the deployment of ambulatory assessment
systems collecting diverse quantitative data, as well as explicit user inputs. Currently
there is a wide range of sensing techniques used especially in wellness and health
monitoring applications. One group of sensing techniques relates to measuring physical
activity and posture. This involves the use of accelerometers, step-counters, gyroscopes
etc. Such means are utilised in studies dealing with specific tasks like appraising
the progress of Parkinson’s disease [11], Multiple Sclerosis [12], detecting falls [13] or even cough monitoring [14]. Moreover, actigraphy is also used for more general purposes, for example the estimation
of energy expenditure and overall activity of the user [15]. These techniques find applications in researching the causes of obesity, stress
and other syndromes that could be related to general physical activity [15].

Depending on the application, ambulatory monitoring systems also incorporate numerous
sensors acquiring physiological data, and almost every behavioural disorder has its
base in a patient’s physiological mechanisms and biological events are important facets
of many cognitive problems [16]. Changes in heart rate, blood pressure, cortisol levels and EEG profiles and other
vital signs, are often a definite indicator of numerous mental conditions [17]. Therefore, collecting momentary physiological data may appear to be a valid application
of ambulatory technology. However, the main barrier to the wide application of physiological
measurements in a real-life ambulatory setting is the cumbersomeness of collecting
such data on a day-to-day basis. This renders ambulatory physiological sensors impracticable.
A more suitable approach is to monitor patients’ activity via other means. The importance
of collecting sensor-based data in addition to traditional self-assessment reports
is of particular importance in dealing with psychiatric and psychological conditions.
Several studies showed that the objective, quantitative data collected via the use
of activity or physiological measurements can differ greatly from the subjective perception
of a self-reporting patient [18]. In general, researchers and practitioners agree that while questionnaires are undoubtedly
suitable as a method for studying subjective attitudes and representations of an experience,
they cannot substitute for actual objective behavioural data coming from everyday
observance [18]. However, applying sensor-based ambulatory monitoring into psychiatry is a relatively
novel field with very few solutions tested and implemented on a wide scale in clinical
practise.

These premises constitute the base for the research presented in this manuscript as
well as other investigations into utilizing technology in tackling bipolar disorder.
Examples of those include the EU Monarca project [19], which origins from earlier works on applying monitoring in facilitating the care
for BD patients [20], as well as the PSYCHE project which aims to use smart garments to collect physiological
data relevant to mental wellbeing [21]. Both of these implementations aim to identify physiological markers for early signs
of an episode. To achieve this, inputs from heart rate to EEG waves would be collected.
As such approach may pose an incompliance risk due to discomfort caused by physiological
sensors, the methods presented in this manuscript aim to rather collect behavioural
data from non-physiological sensors and data collecting techniques.

Methods

Bipolar disorder is characterised by occurrences of depressive and manic episodes,
each with its own specific outcomes. Often the episodes can directly follow each other.
The occurrence of an episode results in major behavioural changes, which are apparent
in all areas of patient’s life [22]. The syndromes include psychomotor agitation, loud speech, self-deprivation of sleep
in manic episodes and psychomotor retardation, indecisiveness, insomnia in the case
of depressive ones.

The established practice of assessing the patient’s wellbeing in bipolar disorder
is to interview the patient regularly. The cooperation with the clinician and well-developed
self-awareness of the person affected by the condition is key to maintaining the desired
mood stability. It is also generally agreed that keeping to a constant life routine
is a major factor in dealing with the disorder. The areas of life likely to be affected
by an onset of an episode can be monitored by pervasive technology. The main research
question for a personalized ambient monitoring system is whether electronically aided
observation could assist in recognising early signs or detect known triggers of an
episode and prompt an intervention aimed at pre-empting an episode and its consequences.

The aim of a sensor network in personalised ambient monitoring is to provide empirical
data to be processed in the core of the system. The sensors should be able to observe
the key areas affected by the condition and its symptoms. This objective data could
then be used to enhance the assessment scale input. The first step was to match expected
bipolar symptoms with possible sensing techniques. Such an approach was also adopted
by Wihelm and Roth in their study on panic disorder [23]. Another premise is to augment the information expected from assessment scales with
an appropriate monitoring technology. Such pairings were made and are shown in Table 1. The pairings were created basing on current diagnostic guidelines [24] as well as consulting psychiatrists and patients. The patient may exhibit only a
subset of the listed symptoms. However, the diagnostic criterion for BD is the occurrence
of at least four of these factors [24].

The techniques proposed in Table 1 require the use of particular sensors. These can be arranged into two sub-groups
depending on the envisioned placement: wearable and environmental. The first refers
to sensors preferably attached to the patient’s body or carried by them throughout
the day. The latter group constitutes of stationary devices monitoring the user’s
home environment and their activity there. Table 2 presents the selected sensors in the said manner. These sensors were then implemented
to create a prototype of a personalized monitoring system.

In the first evaluation phase, the system was put to the test in a technical trial
involving healthy volunteers in order to establish the usability and identify any
fundamental design flaws that may have occurred. The main objective of the trial was
to appraise the sensors’ capability of effectively observing the areas of life influenced
by the course of BD and detecting slight changes in these areas. Although, in healthy
population the observed variations, can be easily attributed to a known cause (e.g.
new fitness regime results in higher physical activity), in BD patients can mean a
relapse of an episode of either kind. Nevertheless – the changes occur in both cases
and testing sensor performance on a group of healthy volunteers was deemed enough
for the initial stage.

The second phase was to interview and recruit potential bipolar patients who were
willing to test the proposed solutions. The technical trial resulted in five installations.
Both trials were granted ethical approval by the UK Research Ethics Committee and
NHS Research and Development office.

Implementation

As stated above, the implemented sensors can be divided into two groups depending
on their target location: environmental i.e. placed in the user’s home and wearable
i.e. worn or carried by the user during the day. The first group utilises a stationary
computer equipped with commercial and custom-designed software acting as a processing
node and storage facility. The latter sensor group is based around a customized mobile
phone serving as a processing and storage node. Moreover, in this scheme the phone
transfers collected data to the computer for backup. The overview of the implemented
prototype is presented in Figure 1.

The wearable sensors consist of an off-the-shelf GPS receiver and a as a belt-worn
box (as seen in Figure 2) containing a 3-axis accelerometer with parameters adjusted to observe body movements,
light detector capable of distinguishing artificial light and a microphone. Both the
GPS receiver and the belt-worn device are Bluetooth enabled to stream data to the
hub mobile phone. The phone’s software was modified in order to acquire process and
store data from the described devices achieved via a custom-made Java ME applet designed
for Java-capable phones. The Bluetooth encounters monitor was a customised applet
implemented on the hub phone monitoring for other Bluetooth-enabled devices in the
immediate surroundings. Matching their unique identification numbers with particular
places and persons (as they might be personal phones of friends and relatives) can
give insight into social activity [28]. The GPS position was provided either via the use of a mobile phone’s internal GPS
receiver or an external Bluetooth enabled device. Among the advantages of the latter
solution was that the overall battery life span was higher compared to that of an
internal receiver.

The environmental sensors were contained in a custom-designed box with several simple
peripheral sensors. Motion detectors were standard passive infra-red (PIR) devices
as used in anti-burglary systems. Door sensors were a simple on/off switches placed
in the most used cupboards within the kitchen area whereas the bed usage sensor was
a simple pressure mat placed under the bedsheets. The remote control monitor utilised
was an infra-red receiver set to detect any standard remote control transmission used
in the vast majority of current entertainment equipment. The sensor did not distinguish
between particular commands (e.g. changing channel, increasing volume) but treated
each as a “remote control event”.

Participants

A total number of four healthy subjects were monitored at the initial stage of the
trial. The participants of this trial were recruited from among people well accustomed
with mobile technology. The reasoning for such an arrangement was that the trial objectives
were mostly technical and could be addressed more efficiently. All participants were
interviewed and gave consent according to the established procedures.

The next stage, following the trial with healthy subjects, was to test out the technology
in the end user scenario. This meant inviting BD sufferers to test the technology
and share their views on all aspects of the proposed system as well as to participate
in a trial similar to the one with healthy volunteers. To facilitate this, a recruitment
process was started in the Southampton and Stirling areas. It involved approaching
patient self-support groups and charities. Following large initial interest during
the process, three participants agreed to the initial home visit but only one participant
proceeded with the full installation as others withdrew before the second visit. The
individuals did not disclose their reasons for not participating and no further contact
was made following their decision in agreement with the ethical guidance obtained.

Results

The main objective of the trial was to evaluate the performance of the sensor network
as well as to assess their potential for monitoring behavioural patterns in BD patients.
Therefore the collected data was pre-analysed in order to determine whether the particular
sensor carried information that may be fed further into a processing algorithm capable
of recognizing the early signs of bipolar episode. Further sub-sections provide a
brief overview of the trial results for wearable and environmental sensors as well
as providing insight into general participant compliance to monitoring regimes. The
presented results come from the trial involving healthy volunteers. The results of
the trial involving the BD patient are covered in a separate section below.

Wearable sensors

Each of the wearable sensors included in the set provided valuable behavioural data.
The belt-worn accelerometer utilised in the technical trial, as expected, provided
information about the subject’s physical activity. It was achieved via subjecting
the raw data resulting from long-term monitoring to a three step algorithm integrating
overall acceleration over a certain period of time – a methodology widely used in
actigraphy [15]. An example of processed data in Figure 3 shows such aggregated activity over the course of two weeks. Missing bars represent
days where the sensor was not worn (amount of data was insufficient). Nevertheless,
the differences in activity of the user can be observed. In particular the user-reported
Sunday workout on 09/12 is apparent.

The wearable light sensor utilized in the study was capable of distinguishing between
natural and artificial sources of light [29]. Its readings were characterised by periods of high natural light level, which can
be accounted for by the user being outdoors in direct sunlight, and periods where
dominance of artificial light is apparent, which indicates the user’s presence indoors.
This dependence can be observed when data is presented against user self-reports.
An example of such is presented in Figure 4.

Location data acquired via the GPS receiver was subjected to clustering aiming to
identify user’s meaningful locations. The GPS data was subjected to pre-processing.
Firstly, all unreliable points with low satellite coverage were removed. Secondly,
since the main aim of the clustering process was to detect significant locations rather
than map the journeys between them, another pre-clustering step was to eliminate points
where the recorded speed (provided by the GPS receiver using its internal calculations)
suggested the subject was moving. The remaining points were subjected to clustering
using a density based algorithm (DBSCAN). This facilitated the discovery of meaningful
locations where point density was directly related to frequency of visits to such
location. Figure 5. presents an example of such clustering. Further details of the GPS data processing
methodology can be found in [28] and [27].

· Identifying the most frequently encountered devices, can provide insight into the
number and times of interaction with users of these devices occurred.

· Frequently encountered devices can be often tied to a one particular location. Therefore,
such encounters can be useful in enhancing the positioning process where GPS localisation
is not available (especially indoors).

· The number of encounters in a single scan can indicate a crowded location like bus
or a city centre. This kind of information can also be useful in appraising the participant’s
social activity.

Environmental sensors

The environmental sensors included in the trial consisted of PIR motion sensors, door
switches and a remote control usage monitor. Each of the devices was activated by
an event (e.g. pressing remote control button) with the exact time of these events
then stored within the system. Figure 6 shows a set of readings representative of a day of monitoring for one of the participants.
Each bar represents an aggregated number of events during a 15 minute interval. In
all cases the results corresponded with self-reporting.

Additionally, monitoring sleep patterns is of particular importance in managing the
course of BD. The sensor utilized in the study, produced two outputs shown in Figures 7 and 8 below. The top part of each figure indicates the user’s presence in the bed. The
bottom one shows the number of pressed/un-pressed events which are likely to be caused
by the user’s muscle activity during the sleep phases. Each bar represents number
of such events during 15 minute long intervals. Increased number of these can indicate
restlessness or night terrors – a key indicator of depression [22]. Figure 7 shows a regular sleeping cycle whereas Figure 8 presents data collected during a disturbed sleep for one of the participant.

Patient trial results

In terms of sensory data, the patient trial produced activity data similar to the
sets obtained from the main trial. Since the patient remained euthymic (asymptomatic)
throughout the monitoring period, there were no major aberrations from her usual routine.
However, the data collected by the wearable node, which depends most on user compliance
with a maintenance regime, was far scarcer than from any participant of the technical
trial. In one of the intermediate interviews, the patient addressed the issue of not
adhering to the routine citing the following reasons:

· Discomfort of carrying extra devices in addition to the mobile phone.

· Lack of familiarity with personal technology in general (not only the PAM system)
resulting in underdeveloped user habits.

Figure 9 presents a summary of how well the participant adhered to maintain separate elements
of the wearable setup. The figure shows the uplink times for the devices during a
period of most intensive usage of the wearable setup.

It can be observed that while the phone was on and monitoring, the remaining elements
were either switched off or remained out of Bluetooth range during extended periods.
This was typically due to the user carrying the phone but not the rest of the setup.

Problems and limitations

As far as the environmental setup did not cause any discomfort to the users, the wearable
sensors, although providing large quantities of behavioural data, exhibited significant
periods of inactivity. This relates to the issue of user adherence as all participating
users reported a level of discomfort caused by the number of devices that needed to
be carried. The fact that the preferred placement of the wearable node should provide
it with exposure to the ambient light sources, only added to the problem.

Overall, the main limitation of the work presented in this manuscript is its relatively
small scale of the trials. The cohort of participants is too small to derive a detailed
behaviour monitoring scheme. Moreover, the high rejection rate among the interviewed
patients indicates that the system may present itself as too obtrusive. However, the
general notions regarding compliance issues and observations regarding the collected
sensory data remain valid and constitute a good base for further investigations in
the field.

Conclusions

In this paper we describe the deployment and evaluation of a personalised ambient
monitoring prototype that included several sensory devices either worn by the users
or placed in their home environment for a purpose of monitoring of onsets of bipolar
disorders. Every evaluated element provided potentially useful data about the users’
activity and behavioural patterns. However, the main and most persistent issue discovered
throughout the tests was the impracticality of maintaining and “wearing” as many devices
on a day to day basis. Such concerns did not apply to environmental sensors as they
require minimal maintenance and, aside from the bed usage sensor, caused neither discomfort
nor unease. Considering this, a successful wearable monitoring system should not interfere
with the user’s routine and habits. Therefore pursuing wearable monitoring via only
a mobile phone in connection with a set of simple unobtrusive environmental sensors
is a logical choice for further investigations.

Nevertheless, the main conclusion arising from this and similar studies in the field
is that despite the technology being developed and sophisticated enough to provide
independent insight into patient’s behaviour, psychiatrists and psychologists are
yet to adopt such means into their general practise. This contrasts with other fields
of medicine where the use of monitoring and diagnostic technology is well established.

Overcoming this barrier requires cooperation between researchers, clinicians and policymakers.
Research needs to involve and inform the psychiatric community of positive outcomes
of implemented studies. Only then can modern technology-based approaches to mental
care can be successfully rolled out and their true value appraised.

Competing interests

The authors declare that they have no competing interests.

Authors’ contribution

PP and JC devised the study and its elements, whereas KW provided background information
and information on user needs. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank the EPSRC, UK for their funding of this project (Grant:
EP/F003714/1) as well as Sally Brailsford, Christopher James, Evan Maggil, Jesse Blum
and James Amor for contributions to conducting the study.